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06_03_gradient_torch_loss_optim.py

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+# prediction            manual
+# gradient computation  autograd
+# loss computation      pytorch loss        -> loss function gets replaced by pytorch function
+# parameter update      pytorch optimizer   -> update weights gets replaced by optimizer.step()
+
+import torch
+import torch.nn as nn # neural network module
+
+
+# linear regression, no bias
+# f = w*x
+# f = 2*x
+
+X = torch.tensor([1, 2, 3, 4], dtype=torch.float32)
+Y = torch.tensor([2, 4, 6, 8], dtype=torch.float32)
+
+w = torch.tensor(0.0, dtype=torch.float32, requires_grad=True) #requires grad for gradient
+
+# model prediction
+def forward(x):
+    return w*x
+
+print(f'Prediction before training: f(5) = {forward(5):.3f}')
+
+#Training
+learning_rate = .01
+n_iters = 100
+
+loss = nn.MSELoss() # use pytorch built in MSE loss function
+optimizer = torch.optim.SGD([w], lr = learning_rate) # use pytorch built in optimizer to optimize parameter 'w' with learning rate
+
+
+for epoch in range(n_iters):
+    # prediction = forward pass
+    y_pred = forward(X)
+
+    # loss
+    l = loss(Y, y_pred)
+
+    # gradients = backward pass
+    l.backward()
+
+    #update weights
+    optimizer.step()
+
+    # clear gradients
+    optimizer.zero_grad()
+
+    if epoch % 10 == 0: #every nth epoch
+        print(f'epoch {epoch+1}: w = {w:.3f}, loss = {l:.8f}')
+
+print(f'Prediction after training: f(5) = {forward(5):.3f}')